Recent advances in artificial intelligence and deep learning have made it possible for bots to pass as humans, as is the case with the recent Google Duplex—an automated voice assistant capable of generating realistic speech that can fool humans into thinking they are talking to another human. Such technologies have drawn sharp criticism due to their ethical implications, and have fueled a push towards transparency in human–machine interactions. Despite the legitimacy of these concerns, it remains unclear whether bots would compromise their efficiency by disclosing their true nature. Here, we conduct a behavioural experiment with participants playing a repeated prisoner’s dilemma game with a human or a bot, after being given either true or false information about the nature of their associate. We find that bots do better than humans at inducing cooperation, but that disclosing their true nature negates this superior efficiency. Human participants do not recover from their prior bias against bots despite experiencing cooperative attitudes exhibited by bots over time. These results highlight the need to set standards for the efficiency cost we are willing to pay in order for machines to be transparent about their non-human nature.

人工智能和深度学习的最新进展使机器人有可能像人类一样通过，就像最近的Google Duplex（自动语音助手）一样，它能够生成逼真的语音，使人们误以为他们在跟另一个人说话。此类技术由于其伦理意义而受到了尖锐的批评，并推动了人机交互的透明性。尽管这些担忧具有正当性，但尚不清楚机器人是否会通过披露其真实本性来损害其效率。在这里，我们进行了行为实验，参与者在获得有关其同伙性质的正确或错误信息后，与人或机器人玩反复的囚徒困境游戏。我们发现，在诱导合作方面，机器人的表现要好于人类，但揭示其真实本性却抵消了这种优越的效率。尽管随着时间的流逝，机器人逐渐表现出合作态度，但人类参与者并没有从对机器人的偏见中恢复过来。这些结果突出表明，有必要为我们愿意支付的效率成本设定标准，以使机器对非人性具有透明性。